Annotate an item by sketching or drawing on it using your nearby iPhone or iPad . If both devices are nearby, click , then choose a device. The tool may appear highlighted to show your device is connected. To disconnect your device without using it, click the tool again.
This article contains everything an Art student needs to know about drawing in one point perspective. It includes step-by-step tutorials, lesson plans, handouts, videos and free downloadable worksheets. The material is suitable for middle and high school students, as well as any other person who wishes to learn how to draw using single point perspective. It is written for those with no prior experience with perspective, beginning with basic concepts, before working towards more complex three-dimensional forms.
The following tutorial explains how to draw one point perspective step-by-step. The exercises are designed to be completed in the order given, with each one building upon the previous task. All worksheets are available as a free perspective drawing PDF that can be printed at A4 size (more worksheets will be added to this over time).
A ruler and compass can be useful while learning to draw in one point perspective, however most Art students find that these exercises are best completed freehand, with dimensions and proportions gauged by eye. This is so that the skills are easily transferrable to an observational drawing.
The mandate to use SciENcv only for preparation of the biographical sketch will go into effect for new proposals submitted or due on or after October 23, 2023. In the interim, proposers may continue to prepare and submit this document via use of SciENcv or the NSF fillable PDF. NSF, however, encourages the community to use SciENcv prior to the October 2023 implementation.
NSF requires a biographical sketch for each individual identified as senior personnel. See the Proposal and Award Policies and Procedures Guide (PAPPG) Chapter II.D.2.h(i) for complete coverage on the content and formatting requirements for the biographical sketch.
A table entitled, NSF Pre-award and Post-award Disclosures Relating to the Biographical Sketch and Current and Pending (Other) Support, has been developed to provide helpful reference information regarding pre-award and post-award disclosure information in the biographical sketch and current and pending support proposal sections. The table identifies where these disclosures must be provided in proposals as well as in project reports.
Thank you for this wonderful book. I am a biology teacher in Germany and use the book as inspiration for my field trips with children and teenagers. It is very well designed, educationally excellent and just fun to read! Thank you for making the download of the book available for free. I also bought the book as a physical copy at the regional bookstore, but the pdf helps me tremendously in my preparations.
Create and share your drawings in real-time with our collaborative sketch board. Choose from a variety of drawing tools and customize them to your liking. You can draw on images, photos, maps, or PDF files. Upload your own images from your clipboard, desktop, or camera. With our online sketch pad, you can freestyle over any image or map and easly create beautiful drawings together with your friends or colleagues.
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In a past #Ditchbook chat, guest moderators Jon and Marlena, creators of EduProtocols and authors of The EduProtocol Field Guide Series, showered us with loads of FREE resources. And of course, the #Ditchbook community shared tons of great tips, ideas, and resources of their own!
Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( ).
When BLAST was first published in 1990 , there were less than 50 million bases of nucleotide sequence in the public archives ; now a single sequencing instrument can produce over 1 trillion bases per run . New methods are needed that can manage and help organize this scale of data. To address this, we consider the general problem of computing an approximate distance between two sequences and describe Mash, a general-purpose toolkit that utilizes the MinHash technique  to reduce large sequences (or sequence sets) to compressed sketch representations. Using only the sketches, which can be thousands of times smaller, the similarity of the original sequences can be rapidly estimated with bounded error. Importantly, the error of this computation depends only on the size of the sketch and is independent of the genome size. Thus, sketches comprising just a few hundred values can be used to approximate the similarity of arbitrarily large datasets. This has important applications for large-scale genomic data management and emerging long-read, single-molecule sequencing technologies. Potential applications include any problem where an approximate, global distance is acceptable, e.g. to triage and cluster sequence data, assign species labels, build large guide trees, identify mis-tracked samples, and search genomic databases.
Mash enables scalable whole-genome clustering, which is an important application for the future of genomic data management, but currently infeasible with alignment-based approaches. As genome databases increase in size and whole-genome sequencing becomes routine, it will become impractical to manually assign taxonomic labels for all genomes. Thus, generalized and automated methods will be useful for constructing groups of related genomes, e.g. for the automated detection of outbreak clusters . To illustrate the utility of Mash, we sketched and clustered all of NCBI RefSeq Release 70 , totaling 54,118 organisms and 618 Gbp of genomic sequence. The resulting sketches total only 93 MB (Additional file 1: Supplementary Note 1), yielding a compression factor of more than 7000-fold versus the uncompressed FASTA (674 GB). Further compression of the sketches is possible using standard compression tools. Sketching all genomes and computing all ~1.5 billion pairwise distances required just 26.1 and 6.9 CPU h, respectively. This process is easily parallelized, which can reduce the wall clock time to minutes with sufficient compute resources. Once constructed, additional genomes can be added incrementally to the full RefSeq database in just 0.9 CPU s per 5 MB genome (or 4 CPU min for a 3 GB genome). Thus, we have demonstrated that it is possible to perform unsupervised clustering of all known genomes and to efficiently update this clustering as new genomes are added.
Mash can also replicate the function of k-mer based metagenomic comparison tools, but in a fraction of the time previously required. The metagenomic comparison tool DSM, for example, computes an exact Jaccard index using all k-mers that occur more than twice per sample . By definition, Mash rapidly approximates this result by filtering unique k-mers and estimating the Jaccard index via MinHash. COMMET also uses k-mers to approximate similarity, but attempts to identify a set of similar reads between two samples using Bloom filters [33, 34]. The similarity of two samples is then defined as the fraction of similar reads that the two datasets share, which is essentially a read-level Jaccard index. Thus, both DSM and COMMET report Jaccard-like similarity measures, which drop rapidly with increasing divergence, whereas the Mash distance is linear in terms of the mutation rate, but becomes less accurate with increasing divergence. Figure 5a replicates the analysis in Maillet et al.  using both Mash and COMMET to cluster Global Ocean Survey (GOS) data . On this dataset, Mash is over tenfold faster than COMMET and correctly identifies clusters from the original GOS study. This illustrates the incremental scalability of Mash where the primary overhead is sketching, which occurs only once per each sample. After sketching, computing pairwise distances is near instantaneous. Thus, Mash avoids the quadratic barrier usually associated with all-pairs comparisons and scales well to many samples. For example, COMMET would require 1 h to add a new GOS sample to this analysis, compared to less than 1 min for Mash.
Future applications of Mash could include read mapping and metagenomic sequence classification via windowed sketches or a containment score to test for the presence of one sequence within another . However, both of these approaches would require additional sketch overhead to achieve acceptable sensitivity. Improvements in database construction are also expected. For example, rather than storing a single sketch per sequence (or window), similar sketches could be merged to further reduce space and improve search times. Obvious strategies include choosing a representative sketch per cluster or hierarchically merging sketches via a Bloom tree . Finally, both the sketch and dist functions are designed as online algorithms, enabling, for example, dist to continually update a sketch from a streaming input. The program could then be modified to terminate when enough data have been collected to make a species identification at a predefined significance threshold. This functionality is designed to support the analysis of real-time data streams, as is expected from nanopore-based sequencing sensors . 2b1af7f3a8